The Model Context Protocol (MCP) is an open standard developed to standardize how artificial intelligence systems, particularly large language models (LLMs), connect and interact with external data sources, tools, and services[1][2][3][4]. Its primary goal is to eliminate the need for custom, vendor-specific integrations by providing a universal interface—much like how USB-C standardizes hardware connections—for AI applications to access and share data.

Key Features and Architecture

• Client-Server Model: MCP uses a client-server architecture. The MCP client (such as an AI assistant or IDE) connects to one or more MCP servers. These servers expose specific tools or data sources—such as file systems, databases, APIs, or business applications—through a standardized protocol[4][5][6].
• Two-way Communication: The protocol enables secure, two-way communication between AI-powered tools and data sources, allowing LLMs to read files, execute functions, and handle contextual prompts in a standardized way[2][5].
• Open Source and Extensible: MCP is open-source, with SDKs available in multiple languages (TypeScript, Python, Java, Kotlin, C#), and a growing repository of pre-built server integrations for popular platforms like Google Drive, Slack, GitHub, and Postgres[3][4].
• Vendor-neutral: MCP is supported by major AI providers (Anthropic, OpenAI, Google DeepMind) and is designed to work across different LLM vendors, giving developers flexibility and reducing lock-in[1][2][4].

How It Works

1. User Request: The user initiates a task via an MCP client (e.g., asks an AI assistant to fetch a file or query a database).
2. Capability Discovery: The MCP client knows which tools and capabilities are available through its connected MCP servers.
3. LLM Orchestration: The client passes the request and available tools to the LLM, which selects the appropriate tool and parameters.
4. Task Execution: The client sends the task to the relevant MCP server, which performs the operation (locally or remotely) and returns the results.
5. Response Generation: The client relays the result to the LLM, which generates a user-facing response[5][6].

Security Considerations

• Local vs. Remote Servers: MCP servers can run locally (on the user’s machine) or remotely (in the cloud or on third-party infrastructure). Security risks and controls differ based on where the server runs, with local servers typically having greater access to sensitive data and system resources[5].

Use Cases

• Connecting AI to business tools, content repositories, and development environments
• Building agentic workflows and automation
• Enabling AI assistants to perform complex, multi-step tasks across diverse data sources

In summary, MCP is a foundational protocol that simplifies and secures the way AI systems interact with the external world, making AI-powered workflows more scalable, flexible, and interoperable[1][2][3][4][5].

Sources
[1] Model Context Protocol – Wikipedia https://en.wikipedia.org/wiki/Model_Context_Protocol
[2] Introducing the Model Context Protocol – Anthropic https://www.anthropic.com/news/model-context-protocol
[3] Model Context Protocol – GitHub https://github.com/modelcontextprotocol
[4] Model Context Protocol: Introduction https://modelcontextprotocol.io
[5] Model Context Protocol (MCP): Understanding security risks and … https://www.redhat.com/en/blog/model-context-protocol-mcp-understanding-security-risks-and-controls
[6] MCP (Model Control Protocol) – CodeGPT https://docs.codegpt.co/zh-Hans/docs/tutorial-features/mcp
[7] Model Context Protocol (MCP), clearly explained (why it matters) https://www.youtube.com/watch?v=7j_NE6Pjv-E